Subsequent work where the model (REF ) and variants of it were considered, has focused on regularized estimators in order to adapt to noise or to generalize the required assumptions. First results for the model (REF ) were obtained by [1]}, where a convex program was proposed and investigated. If \(\beta ^*\)  is exactly \(s\) -sparse, i.e. it has at most \(s\)  non-zero entries, the attainable convergence rates can be improved and faster performance guarantees were obtained by [2]}, [3]}, [4]}. Further work investigated non-Gaussian measurements [5]}, active learning [4]}, [7]}, [8]} and random shifts of \(\langle X_i, \beta ^*\rangle \) , called dithering, [9]}, [10]}.
